DRAC: a dynamically reconfigurable active L1 cache model for hybrid prototyping of multicore embedded systems
Bibliographic record
Abstract
This paper presents a novel dynamically reconfigurable active L1 cache model for hybrid prototyping, called DRAC. The hybrid prototyping technique simulates a multicore embedded system using an emulation kernel on top of a single physical instance of a core. We extend hybrid prototyping by supporting memory hierarchy modeling with DRAC. The presented cache model is a standalone cycle accurate model that is further customized for multicore emulation. DRAC run-time configurability enables the embedded system designer to simulate and explore different multicore design options without the need for full FPGA prototyping. Our experimental results show 2.78% average error and 5.06% worst case error when DRAC is used as a standalone cache model in a single core design. We also observed 100% relative accuracy and less than 13% absolute worst case error in timing estimation when DRAC is used for hybrid prototyping of multicore designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".